Epistemic Network Analysis in Data Studies
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Epistemic Network Analysis
Week 5 Video 6
Today’s Class
Epistemic Network Analysis
Epistemic Network Analysis (ENA)
(Shaffer, 2017)
Studying relationships between elements in coded
data
Lots of applications
Conference founded around this method
(in large part)
International Conference on Quantitative Ethnography
Nodes and links
Nodes = occurrences of the codes
Links = co-occurrences of the codes
Let’s start with an example
Chosen primarily because I understand it well
Analyzing Quitting Behavior
(Karumbaiah et al., 2019)
Comparing students who quit a level in the game
Physics Playground
to students who do not quit a
game level
In terms of the gameplay actions each group of
students makes
Nodes and links
Nodes are behaviors
Links represent when a player demonstrates both
behaviors in one session playing one level
Nodes and links
When red students draw.freeform, they also erase
Less commonly, when they draw.freeform, they also
nudge
When green students
draw.freeform, they also ramp
Less commonly, when they
nudge, they also ramp
Comparing groups in data
In this case,
red= people who quit a game
green = people who do not quit
Can Compare Graphs Between
Contexts (here: game levels)
Interpreting the graphs in
(Karumbaiah et al., 2019)
Can seem tricky
Very powerful when you dig into the graphs
Key Themes
identified by Karumbaiah
et al. (2019)
Identifying Key Action
Missing Identification of Supporting Objects
Over-reliance on Nudge
Limited Early Action Expansion and Later Action
Convergence
Identifying Key Action
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Missing Identification of Supporting
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Over-reliance on Nudge
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Limited Early Action Expansion and
Later Action Convergence
Need Fulcrum
Note
We looked at these graphs qualitatively, but
statistical analysis of differences is possible too
Is link A stronger than link B?
Is link Q stronger in group R or group S?
Other examples
Studying connections between topics in
meetings over time (Nash & Shaffer, 2013)
Studying Process of Successful and Unsuccessful
Teams (Arastoopour et al., 2016)
Exploring Shifts in Student Identity over
Time (Barany & Foster, 2019)
Important setup questions
What makes a relationship “stronger”?
Important setup questions
What are your codes?
How did you derive those codes?
Behaviors in data
Text mining
Hand coding
Hand coding THEN text mining (nCoder+)
(Cai et al., 2019)
Collaborative mixed-initiative human-machine coding
(Codey) (Choi et al., 2022)
Important setup questions
Which codes do you display?
What are your aggregation units (stanzas)?
Everything a learner does together
Everything a learner does on a specific level together
Everyone in a group of learners/team
Everything in a piece of content
Everything in a meeting
Referred to as Stanza-Based
Interaction Data (Shaffer et al., 2016)
1.
A set of objects
2.
The way they relate to each other
3.
Grouped into a set of stanzas
4.
That reveal evidence about the relationships
between the objects
Important setup questions
One-directional relationships or bi-directional
relationships?
Usually bi-directional, but some work looks at one-
directional relationships over time
(Karumbaiah & Baker, 2020)
Important setup questions
What do the X and Y axes mean?
Typically determined empirically by collapsing the
feature space using SVD, singular value decomposition
Similar to factor analysis (week 7)
This approach can make X and Y hard to interpret but
best splits out the variables visually
ENA
Important method, growing in scope and community
applying it
Knowledge Graphs/Spaces
Another key application of network analysis
We will discuss next week
Next week
Structure Discovery
Epistemic Network Analysis (ENA) is a method for studying relationships between elements in coded data with various applications. It involves analyzing nodes as occurrences of codes and links as co-occurrences of codes. Using examples like analyzing quitting behavior in a game, ENA can compare groups in data and interpret graphs to glean valuable insights. The International Conference on Quantitative Ethnography is a prominent platform for discussions centered around ENA methodology.
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Presentation Transcript
Week 5 Video 6 Epistemic Network Analysis
Todays Class Epistemic Network Analysis
Epistemic Network Analysis (ENA) (Shaffer, 2017) Studying relationships between elements in coded data Lots of applications Conference founded around this method (in large part) International Conference on Quantitative Ethnography
Nodes and links Nodes = occurrences of the codes Links = co-occurrences of the codes
Lets start with an example Chosen primarily because I understand it well
Analyzing Quitting Behavior (Karumbaiah et al., 2019) Comparing students who quit a level in the game Physics Playground to students who do not quit a game level In terms of the gameplay actions each group of students makes
Nodes and links Nodes are behaviors Links represent when a player demonstrates both behaviors in one session playing one level
Nodes and links When red students draw.freeform, they also erase Less commonly, when they draw.freeform, they also nudge When green students draw.freeform, they also ramp Less commonly, when they nudge, they also ramp
Comparing groups in data In this case, red= people who quit a game green = people who do not quit
Can Compare Graphs Between Contexts (here: game levels)
Interpreting the graphs in (Karumbaiah et al., 2019) Can seem tricky Very powerful when you dig into the graphs
Key Themes identified by Karumbaiah et al. (2019) Identifying Key Action Missing Identification of Supporting Objects Over-reliance on Nudge Limited Early Action Expansion and Later Action Convergence
Identifying Key Action Indicates their lack of conceptual understanding of Physics
Over-reliance on Nudge Indicates potential wheel spinning tendencies
Limited Early Action Expansion and Later Action Convergence Need Fulcrum
Note We looked at these graphs qualitatively, but statistical analysis of differences is possible too Is link A stronger than link B? Is link Q stronger in group R or group S?
Studying connections between topics in meetings over time (Nash & Shaffer, 2013)
Studying Process of Successful and Unsuccessful Teams (Arastoopour et al., 2016)
Exploring Shifts in Student Identity over Time (Barany & Foster, 2019)
Important setup questions What makes a relationship stronger ?
Important setup questions What are your codes? How did you derive those codes? Behaviors in data Text mining Hand coding Hand coding THEN text mining (nCoder+) (Cai et al., 2019) Collaborative mixed-initiative human-machine coding (Codey) (Choi et al., 2022)
Important setup questions Which codes do you display? What are your aggregation units (stanzas)? Everything a learner does together Everything a learner does on a specific level together Everyone in a group of learners/team Everything in a piece of content Everything in a meeting
Referred to as Stanza-Based Interaction Data (Shaffer et al., 2016) A set of objects The way they relate to each other Grouped into a set of stanzas That reveal evidence about the relationships between the objects 1. 2. 3. 4.
Important setup questions One-directional relationships or bi-directional relationships? Usually bi-directional, but some work looks at one- directional relationships over time (Karumbaiah & Baker, 2020)
Important setup questions What do the X and Y axes mean? Typically determined empirically by collapsing the feature space using SVD, singular value decomposition Similar to factor analysis (week 7) This approach can make X and Y hard to interpret but best splits out the variables visually
ENA Important method, growing in scope and community applying it
Knowledge Graphs/Spaces Another key application of network analysis We will discuss next week
Next week Structure Discovery
